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11 Oct 2022 15:01:56 UTC
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Create Impactful 3D Bar Graphs for better data visualization
To understand the numerical or data specific relationship between 3 different classes of information, 3D bar graphs are one of the most impactful techniques in data visualization.
In this tutorial we will cover applying Pandas, matplatlib, mplot3d and numpy in python to create 3D bar graphs. We have used both Jupyter notebook and python source files (.py) to first learn how to create the graphs and then finalize the code into a function which can be used with any source dataset along with selected 3 columns to render their relationship into X, Y and Z axis.
We have used the following 3 dataset for show the 3D bar graph visualization:
- Supermarket Sales Data
- NFL TV viewership and ratings Data
- Titanic passenger Data
Video Timestamps are as below:
- (00:00) Start of the Video
- (0:06) Intro
- (3:04) NFL Superbowl TV viewership and ratings Data
- (5:15) Supermarket Sales Data
- (8:10) Titanic passenger Data
- (14:25) Jupyter Notebook Python Code walkthrough
- (25:01) Recap
Dataset GitHub URL:
https://github.com/prodramp/publiccode/tree/master/datasets
Python Code (Jupyter Notebook) GitHub Url:
https://github.com/prodramp/publiccode/tree/master/pandas/3d-bar-plotting
Please visit:
https://prodramp.com
@prodramp
https://www.linkedin.com/company/prodramp
Content Creator:
Avkash Chauhan (@avkashchauhan)
https://www.linkedin.com/in/avkashchauhan
Tags:
#webdevelopment, #frontend #react, #chakraui, #layout, #fullstackdevelopment #pandas #dataanalysis #python #bigdata #machinelearning #artificialintelligence #ai #ml #dataanalysis #datavisualization #pandas #3d #3dbar #3dbargraph #matplotlib #mplot3d
...
https://www.youtube.com/watch?v=eKtYrGlQ-AM
In this tutorial we will cover applying Pandas, matplatlib, mplot3d and numpy in python to create 3D bar graphs. We have used both Jupyter notebook and python source files (.py) to first learn how to create the graphs and then finalize the code into a function which can be used with any source dataset along with selected 3 columns to render their relationship into X, Y and Z axis.
We have used the following 3 dataset for show the 3D bar graph visualization:
- Supermarket Sales Data
- NFL TV viewership and ratings Data
- Titanic passenger Data
Video Timestamps are as below:
- (00:00) Start of the Video
- (0:06) Intro
- (3:04) NFL Superbowl TV viewership and ratings Data
- (5:15) Supermarket Sales Data
- (8:10) Titanic passenger Data
- (14:25) Jupyter Notebook Python Code walkthrough
- (25:01) Recap
Dataset GitHub URL:
https://github.com/prodramp/publiccode/tree/master/datasets
Python Code (Jupyter Notebook) GitHub Url:
https://github.com/prodramp/publiccode/tree/master/pandas/3d-bar-plotting
Please visit:
https://prodramp.com
@prodramp
https://www.linkedin.com/company/prodramp
Content Creator:
Avkash Chauhan (@avkashchauhan)
https://www.linkedin.com/in/avkashchauhan
Tags:
#webdevelopment, #frontend #react, #chakraui, #layout, #fullstackdevelopment #pandas #dataanalysis #python #bigdata #machinelearning #artificialintelligence #ai #ml #dataanalysis #datavisualization #pandas #3d #3dbar #3dbargraph #matplotlib #mplot3d
...
https://www.youtube.com/watch?v=eKtYrGlQ-AM
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video/mp4
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English
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VIDEO
PYTHO
python-gradio-ui-for-fine-tuning-llm
This video is hands-on-lab to create a python application using Gradio UI to fine-tuning OpenAI LLM model with llamaindex (or gpt-index) library.
Fine-tuning LLM: LLM can be extended, by fine tuning with custom dataset to provide Q&A, summary and many other ChatGPT like functions. In this video, we are using LlamaIndex (GPT Index) which provides a central interface to connect your LLM's with external data.
The full code for the Gradio UI is hosted here:
- https://github.com/prodramp/DeepWorks/blob/main/ChatGPT/FineTuneLLM.md
== Video Timeline ==
(00:00) Content Intro
(00:10) Part 1 Recap
(01:14) Gradio UI Design
(05:55) Python Coding Start
(06:46) Tab 1- OpenAI Config UI
(08:15) Tab 2- Fine-tune LLM UI
(08:55) Tab 3- Query Prompt UI
(12:30) Testing UI (without action)
(13:05) Tab 1- Click Action Code
(15:15) Tab 2- Click Action Code
(23:05) Fine-turning with OpenAI UI
(24:50) Fine-turning with OpenAI Run
(28:28) Tab 3- Click Action(s) Code
(33:05) Query with Index UI
(35:30) Full testing with Text Document
(36:18) API Billing and Cost
(38:30) Code Repo and GitHub
(39:04) Summary
Please visit:
https://prodramp.com | @prodramp
https://www.linkedin.com/company/prodramp
Content Creator:
Avkash Chauhan (@avkashchauhan)
https://www.linkedin.com/in/avkashchauhan
Tags:
#llm #chatgpt #finetunellm #openai #python #ai
...
https://www.youtube.com/watch?v=s4cQsNPpgzs
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Created
1 month ago
Content Type
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video/mp4
English
Controlling
VIDEO
HANDS
hands-on-tutorial-to-use-hindi-on
Slow and detailed hands-on-tutorial for non technical Android users:
This tutorial I created from a request from parents for their academic group members who wants to take advantage of technology to create literature and poetry on Android mobile phones while typing and speech to text with Hindi as main language.
Content Timeline:
- (00:00) Start
- (00:08) Intro
- (03:45) 1. Hindi Keyboard setting for typing and speech to text
- (37:52) 2. Tips for best speech to text typing
- (47:55) 3. Using Google Drive and Docs for content management
- (1:01:40) Recap
Please visit:
------------------
Prodramp LLC | https://prodramp.com | @prodramp
https://www.linkedin.com/company/prodramp
Content Creator: Avkash Chauhan (@avkashchauhan)
https://www.linkedin.com/in/avkashchauhan
Tags:
#whatsapp #googledrive #googledocs #hindifonts #hindionandroid
...
https://www.youtube.com/watch?v=SnQRXkgo5ts
Transaction
Created
1 month ago
Content Type
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video/mp4
English
Controlling
VIDEO
YOUR
your-hands-on-guide-to-efficient
This video is your hands on step-by-step guide to use pre-built EG3D-Efficient Geometry-aware 3D Generative Adversarial Networks models of various kind on your Ubuntu 22.04 Linux machine with Python 3.9, PyTorch and various CuDA GPU deep learning libraries.
GitHub Resources:
https://github.com/prodramp/DeepWorks/tree/main/EG3D
https://github.com/NVlabs/eg3d
▬▬▬▬▬▬ ⏰ TUTORIAL TIME STAMPS ⏰ ▬▬▬▬▬▬
- (00:00) Video Start
- (01:10) Content Intro
- (03:15) Setting Environment for EG3D
- (06:00) Downloading Model
- (07:00) Loading Model Visualizer
- (09:30) Visualizing AFHQ Cat Model
- (15:05) Visualizing Sports Car Model
- (15:40) Exporting Video from Models
- (17:00) Generating 3D Shapes
- (17:35) Loading 3D Shapes in ChimeraX
- (18:45) 3D Shape and Volume Movies
- (20:15) GitHub Resources
Connect
------------------
- Prodramp By Avkash (@prodramp)
- Website - https://prodramp.com
- LinkedIn - https://www.linkedin.com/company/prodramp
- GitHub- https://github.com/prodramp/
- AngelList - https://angel.co/company/prodramp
- Facebook - https://www.facebook.com/Prodramp
Content Creator: Avkash Chauhan (@avkashchauhan)
- https://www.linkedin.com/in/avkashchauhan
- https://twitter.com/avkashchauhan
Tags:
#EG3D #nerf #nvidia #ai #ml #lime #aicloud #h2oai #driverlessai #machinelearning #cloud #mlops #model #collaboration #deeplearning #modelserving #modeldeployment #pytorch #datarobot #datahub #streamlit #modeltesting #codeartifact #dataartifact #modelartifact #onnx #aws #kaggle #mapbox #lightgbm #xgboost #classification #dataengineering #pandas #keras #tensorflow #tensorboard #cnn #prodramp #avkashchauhan #LIME #mli #blender #meshlab
...
https://www.youtube.com/watch?v=7tG37T6bsBw
Transaction
Created
1 month ago
Content Type
Language
video/mp4
English
Controlling
VIDEO
AN IM
an-improved-and-functional-design-of-end
Functional overview of ML Life Cycle
---------------------------------------------------------------------------------------
If you are solving a problem using machine learning, no matter if the problem is very small business problem or a super large, the end-to-end machine learning life cycle will be the same. The machine learning life cycle has 4 stages and these 4 stages are:
1. Data Preparation
2. Model training and Tuning
3. Model deployment and monitor
4. Inference or model serving
The internal steps at each life cycle stage could be more or less however there will always be 4 of above stages in any machine learning life cycle.
In this video we are going to cover the functional design of all of these 4 stages as below:
Video Timeline:
-------------------------
(00:00) Video Start
(00:07) Content intro
(02:43) Motivation to this video
(03:41) 4 Stages of ML Life cycle
(06:07) Stage 1: Data Preparation
(11:27) Stage 2: ML Training and Tuning
(13:53) Stage 3: Model Deployment and Monitoring
(20:01) Stage 4: Inference or Model Serving
(22:55) Monitoring and Re-training
(24:10) Recap
(26:10) Credits
GitHub Repo:
URL: https://github.com/prodramp/publiccode
Please visit:
https://prodramp.com | @prodramp
https://www.linkedin.com/company/prodramp
Content Creator:
Avkash Chauhan (@avkashchauhan)
https://www.linkedin.com/in/avkashchauhan
Tags:
#webdevelopment, #frontend #react #chakraui #fullstack #uidevelopment #JavaScript #aws #sagemaker #googleai #azureml #h2oai #ml #ai #datadesign #mlops #mlengineering #modeldeployment #inference #modelmonitoring #datapreparation #modelretraining
...
https://www.youtube.com/watch?v=EeZN2EGqNVI
Transaction
Created
1 month ago
Content Type
Language
video/mp4
English
Controlling
VIDEO
BEGIN
beginners-workshop-to-fine-tune-openai
This hands-on workshop shows how to use Langchain LLM application framework with Chroma embedding database to fine-tune an OpenAI GPT-3.5-Turbo LLM model on web data. The final solution returns ChatGPT like interface to your customer web data.
You will also learn:
- Why it is important to fine-tune LLM models with ad-hoc data
- How to use open-source libraries i.e Langchain, ChromaDB
The workshop code:
- https://github.com/prodramp/DeepWorks/blob/main/ChatGPT/LangChainOpenAI.md
== Video Timeline ==
(00:00) Content Intro
(00:58) The Problem
(04:12) The Solution
(06:13) Working Solution Demo
(09:10) Understanding Solution
(12:12) Open-source libs
(14:22) Web Data as source content
(12:30) Testing UI (without action)
(15:58) Python Libs Installed
(17:08) Python Coding Starts
(17:27) Setting OpenAI API Key
(18:16) Setting Embeddings
(20:12) Setting Chunk Splitter
(21:02) Setting embeddings Model
(21:43) Create & Persist Embeddings
(23:30) Test Embeddings Code
(26:08) Setting Langchain App
(27:30) Adding Query to KB DB
(29:18) Testing Query with KB
(30:24) Continuous Queries
(32:08) Opensource Libs Review
(32:41) OpenAI Billing
(33:34) Source code
(34:00) Recap
=== Open-source Libraries Used: ===
- https://github.com/chroma-core/chroma
- https://github.com/hwchase17/chat-langchain
- https://github.com/openai
Please visit:
https://prodramp.com | @prodramp
https://www.linkedin.com/company/prodramp
Content Creator:
Avkash Chauhan (@avkashchauhan)
https://www.linkedin.com/in/avkashchauhan
Tags:
#llm #chatgpt #finetunellm #openai #python #ai #langchain #chromadb
...
https://www.youtube.com/watch?v=JB1VT7zvEII
Transaction
Created
1 month ago
Content Type
Language
video/mp4
Controlling
VIDEO
DATA
data-engineering-and-data-storage
This video discussed the following topics:
- Data Stack (Modern and Post-Modern)
- Data Storage and Analysis
- Data Lake, Data Warehouse, Lake House
- ETL, ELT, and when to use it
- Data Transformation and Pipeline
- Data Mesh
== Video Timeline ==
(00:00) Content Intro
(00:17) What is answered?
(01:45) Part 1 Content
(03:05) Data Storage and Analysis
(06:25) Data Warehouse
(08:30) Data Lake
(10:08) Lake House
(12:04) Data Pipeline
(13:55) Data Warehouse Pipeline
(13:55) Data Lake Pipeline
(17:00) ETL
(18:38) ELT
(18:56) ETL & ELT Example
(21:25) ETL & ELT Best Use
(22:40) Snowflake Data Warehouse
(23:47) Transformation in Data Warehouse
(24:20) Data Transformation Pipeline in Data Warehouse
(25:18) Data Mesh
(26:38) Conclusion
Please visit:
https://prodramp.com | @prodramp
https://www.linkedin.com/company/prodramp
Content Creator:
Avkash Chauhan (@avkashchauhan)
https://www.linkedin.com/in/avkashchauhan
Tags:
#dataengineering #dataanalytics #datatransformation
...
https://www.youtube.com/watch?v=lWQETCfawKA
Transaction
Created
1 month ago
Content Type
Language
video/mp4
English
Controlling
VIDEO
DATA
data-processing-fundamentals-for-the
Knowing python programming with its application into data processing is one of the most demanding aspect of Python programming language and this video is here to assist you.
- Part 0: Course Introduction (https://www.youtube.com/watch?v=0aw2aFxDmxM)
- Part 1: Data Loading, Reading, and value based Filtering (https://youtu.be/660nOAR2vOM)
- Part 2: Data Filtering by Type with Inline coding (https://youtu.be/uJES3DLg5ss)
- Part 3 (a/b): Writing Smart Python code through classes
- Part 3a - https://youtu.be/7A6VZYuIKAU
- Part 3b - https://youtu.be/4heu1splH64
- Part 4 (a/b): Adding Dynamic GUI to Python Data Application
- Part 4a (https://youtu.be/pEO9c2HLGNE)
- Part 4b (https://youtu.be/2kqwIF14AV8)
Content covered in this video will be located in the following GitHub repo:
- https://github.com/prodramp/DeepWorks/tree/main/DeveloperProgram
▬▬▬▬▬▬ ⏰ TUTORIAL TIME STAMPS ⏰ ▬▬▬▬▬▬
- (00:00) Setup Requirement
- (01:09) First 4 Parts of Course
- (01:40) Data Fundamentals
- (05:35) Data Processing Steps
- (07:25) Python based Data application
- (09:05) Python Packages
- (10:00) Content covered in 4 Parts video
- (13:25) Course Objective
Connect
------------------
- Prodramp LLC (@prodramp)
- Website - https://prodramp.com
- LinkedIn - https://www.linkedin.com/company/prod...
- GitHub- https://github.com/prodramp/
- AngelList - https://angel.co/company/prodramp
- Facebook - https://www.facebook.com/Prodramp
Content Creator: Avkash Chauhan (@avkashchauhan)
- https://www.linkedin.com/in/avkashcha...
- https://twitter.com/avkashchauhan
Tags:
#python #pandas #dataengineering #dataprocessing #deeplearning #seaborn
...
https://www.youtube.com/watch?v=0aw2aFxDmxM
Transaction
Created
1 month ago
Content Type
Language
video/mp4
English
Controlling
VIDEO
DALL-
dall-e-mini-text-to-image-generation
If you just have access to Google Colab (free version), you can use DALL-E mini text to image generation AI model to develop your own synthetic images and perform any experiment you like. This video is all you need to get started...
GitHub Resources:
https://github.com/prodramp/DeepWorks/tree/main/DallE-mini
Research Paper and Code:
- https://github.com/borisdayma/dalle-mini
- https://huggingface.co/spaces/dalle-mini/dalle-mini
▬▬▬▬▬▬ ⏰ TUTORIAL TIME STAMPS ⏰ ▬▬▬▬▬▬
- (00:00) Content Intro
- (02:38) DALL-E mini Intro
- (03:25) DALL-E mini Hugging Face Card
- (03:45) Google Colab Setup
- (04:24) DALL-E mini Source Code
- (05:27) Downloading DALL-E mini model
- (06:27) Downloading VQ-GAN Model
- (08:07) Setting Text Input Prompt
- (09:13) Text to Image Generation Start
- (10:12) Conclusion and GitHub Code
Connect
------------------
- Prodramp LLC (@prodramp)
- Website - https://prodramp.com
- LinkedIn - https://www.linkedin.com/company/prodramp
- GitHub- https://github.com/prodramp/
- AngelList - https://angel.co/company/prodramp
- Facebook - https://www.facebook.com/Prodramp
Content Creator: Avkash Chauhan (@avkashchauhan)
- https://www.linkedin.com/in/avkashchauhan
- https://twitter.com/avkashchauhan
Tags:
#dalle #ai #dalle2 #cnn #ml #lime #aicloud #h2oai #driverlessai #machinelearning #cloud #mlops #model #collaboration #deeplearning #modelserving #modeldeployment #pytorch #datarobot #datahub #streamlit #modeltesting #codeartifact #dataartifact #modelartifact #onnx #aws #kaggle #mapbox #lightgbm #xgboost #classification #dataengineering #pandas #keras #tensorflow #tensorboard #cnn #prodramp #avkashchauhan #LIME #mli #xai
...
https://www.youtube.com/watch?v=emnpYD6-90c
Transaction
Created
1 month ago
Content Type
Language
video/mp4
Controlling
VIDEO
CAN Y
can-you-really-predict-wildfire-hotspots
Wildfires are a global problems for several countries and threaten lives, communities, wildlife, and forests every year. Wildfire also cause significant changes into out climate.
With this wildfire project my objective is to make everyone knowledgeable about how to get historical and latest wildfire data for any country and then use data engineering, visualization and machine learning technology to provide assistance in solving this problem at their own level.
This tutorial is divided into 2 parts as below:
Part 1: Global Wildfire Data Collection
- Harvest data from NASA website
- Create USA/CA/Nepal dataset
- Create any country wildfire dataset
- Visualize any wildfire data with mapboxgl, matplotlib and Kepler.gl
- Build streamlit app to show wildfire
for any country/region by year
Part 2: Wildfire Hotspot Prediction (This tutorial)
- Use California wildfire dataset as source for this project
- Develop wildfire hotspot detection strategy
- feature engineering
- Build ML model with various algorithms/libraries (LightGBM, xgboost, H2O.ai open source ML platform)
- Validate and Test ML Models
- Predict wildfire hotspots
Content Timeline:
----------------------------
- (00:00) Content start
- (00:16) Topic introduction
- (02:12) Data Collection part-1 recap
- (03:05) California Wildfire Dataset
- (04:55) Tutorial Starts
- (05:18) Machine Learning Strategy
- (10:29) Feature Engineering
- (29:20) Final ML Ready Dataset
- (30:47) Wildfire Hotspot Visualization
- (34:38) Google colab notebooks
- (35:22) Wildfire Hotspot Model by LightGBM
- (41:50) Wildfire Hotspot Model by xgboost
- (45:16) Wildfire Hotspot Model by H2O.ai
- (51:36) Recap
- (54:38) Project Completion
How to create wildfire dataset for any country or region:
https://github.com/prodramp/wildfire/blob/main/dataset/README.md
Wildfire Hotspot Prediction ML Models:
https://github.com/prodramp/wildfire/tree/main/ml
Streamlit application to show wildfire data for any country by year:
https://github.com/prodramp/wildfire/tree/main/wildfire-data-streamlit
Download California Wildfire Dataset from Kaggle:
https://www.kaggle.com/datasets/avkashchauhan/california-wildfire-dataset-from-2000-2021
Please visit:
------------------
Prodramp LLC | https://prodramp.com | @prodramp
https://www.linkedin.com/company/prodramp
Content Creator: Avkash Chauhan (@avkashchauhan)
https://www.linkedin.com/in/avkashchauhan
Tags:
#ai #aicloud #h2oai #driverlessai #machinelearning #cloud #mlops #model #collaboration #deeplearning #modelserving #modeldeployment #keras #tensorflow #pytorch #datarobot #datahub #aiplatform #aicloud #modelperformance #modelfit #modeleffect #modelimpact #modelbias #modeldeployment #modelregistery #modelpipeline #neptuneai #pythondsp #pythonaudio #streamlit #pythonapps #deepchecks #modeltesting #codeartifact #dataartifact #modelartifact #onnx #aws #supervisor #ec2 #supervisord #kaggle #wildfire #naturaldisaster #kepler.gl #mapbox mapboxgl #wildfireml #lightgbm #xgboost #classification #regression
...
https://www.youtube.com/watch?v=mlUcDCKSbOs
Transaction
Created
1 month ago
Content Type
Language
video/mp4
English